Method and Arrangement for Loop Qualification in a Digital Subscriber Line (DSL) System

ABSTRACT

A method and loop qualification unit for determining loop parameters of a topology of a twisted pair loop for a digital subscriber line system. The parameters are represented by a vector that receives a measurement of a SELT parameter measured at one end of the loop and a measurement of a DELT loop transfer function measured at both ends of the loop. A first model generator generates a first model for the SELT parameter and a second model generator generates a second model for the DELT function based on the loop parameters represented by the vector. A processor then determines the parameters by minimizing the differences between the first model and the SELT parameter, and the second model and the DELT function. The determined parameters are represented by the vector that provides the minimization.

TECHNICAL FIELD

The present invention relates to a method and arrangement for loopqualification in a digital subscriber line (DSL) system. In particular,the present invention provides a solution for determining loopparameters associated with the DSL by using Single Ended Line Test(SELT) and Double Ended Line Test (DELT) measurements, preferably basedon evolutionary computation.

BACKGROUND

Digital subscriber lines (DSLs) offer carriers the possibility ofexploiting the existing loop plant to deliver high-speed data and voiceservices. Today several types of Digital Subscriber Line (DSL)technologies are rapidly becoming standards for delivering access oncopper access network cables to the end user. Examples of DSLtechnologies (sometimes called xDSL) include High Data Rate DigitalSubscriber Line (HDSL), Asymmetric Digital Subscriber Line (ADSL), andVery-high-bit-rate Digital Subscriber Line (VDSL).

The DSL, which connects the customer premises (CP) to the central office(CO), has several impairments that are not present for the plain oldtelephony service (POTS) because xDSL exploits a much wider frequencyband. As a consequence, the existing POTS testing equipment is notcapable of accurately qualifying a subscriber loop for xDSLtransmission. There are impairments causing attenuation, such as bridgedtaps, mixed wire gauge, and bad splices. In order to qualify asubscriber loop for xDSL transmission it is desired to fullycharacterize the subscriber loop, i.e., to identify its loop makeup. Theloop (=line) make up implies in this description parameters such as thetotal length of the loop, number of sections, length and gauge (i.e. thediameter) of each section, splice location, and number of bridged tapsand their position and length. Loop make-up identification allowstelephone companies to update and correct their loop plant records.Therefore, accurate loop make-up identification can further be used toupdate records in loop databases, and such records can in turn beaccessed to support engineering, provisioning and maintenanceoperations.

In this way, the development of automatic loop makeup techniques is veryimportant for cost reduction during the service deployment stage andeven afterwards, during preventive monitoring tests against eminentservice failures. Nowadays, there are several works that address thisissue, but the majority is focused on single-ended techniques referredto as Single ended Line Test (SELT). The SELT may be based on TimeDomain Reflectometry (TDR). TDR implies an analysis of a loop (wire,cable, or fiber optic) by sending a pulsed signal into the loop, andthen examining the reflection of that pulse. By examining the polarity,amplitude, frequencies and other electrical signatures of allreflections, tampering or bugs may be precisely located.Frequency-domain reflectometry (FDR) is another technique that SELT maybe based on. In FDR, the loop is sounded with a swept sinusoid toidentify frequencies that either resonate or are “dead.” For example,peaks in the measured receive signal correspond to frequencies thatcreate standing waves. Standing wave frequencies provide informationabout the length of the cable.

Moreover, SELT may also be based on a parameter referred to as One-PortScattering Parameter, denoted S11 or echo response: This method issimilar to the FDR, but instead of looking for individual frequencies, acomplete echo response measurement is utilized. From the echo response,the input impedance or S11 of the loop can be determined, from which theloop topology can be determined.

With the advent of G.992.3 and G.992.5 standards for ADSL 2 and ADSL 2+,modems with the function loop-diagnostic became possible. These modemslocated at the user side, jointly with the IPDSLAM (Internet ProtocolDigital Subscriber Line Access multiplexer) located at the CO-side,enable measurement of the attenuation per tone, referred to as thetransfer function, directly. As it is possible to have two portmeasurements, it is possible to determine the ratio between the signalat input and output of the line and thus a measurement of the transferfunction can be obtained. This new functionality brings forth theperspective of new, reliable and precise techniques for loop makeupidentification and supervising. Such two-terminal measurements arereferred to as DELT (Double Ended Line Test), in contrast to the SELT.

The most common qualification method concentrates on mining on theexisting data in loop databases, checking its accuracy, and thenbulk-provisioning loops that are candidates for DSL-based service.Sometimes a combination of loop records and engineering informationabout feeder route topology is used to obtain an estimate of looplength. This technique presents quite imprecise estimative. Often suchdata are not reliable or non-existing. Furthermore, manual LoopQualification (LQ) with human intervention is costly and open up forhuman errors.

A great number of articles about loop qualification (LQ) methods arebased on TDR data obtained from SELT measurements. Previous attempts touse TDR techniques, sometimes coupled to artificial neural networkalgorithms, have failed due to the difficulty of the post-processing ofthe TDR trace needed to extract all loop features. Moreover,conventional metallic TDRs are not capable of detecting all reflections.In fact, conventional metallic TDRs cannot detect gauge changes and,moreover, have a serious range limitation that prevents them fromdetecting reliably echoes further than several kilometers (km) from theCentral Office (CO). Besides, it is necessary with additional processingof the TDR data because accurate TDR measurements alone are notsufficient without an algorithm able to extract information from the TDRtrace (i.e. TDR plot or curve). That implies that the additional timefor this processing is required and the processing of the TDR data isnot trivial and could be subjective, making automation of this techniquevery difficult. In particular, a major problem arises in a TDR approachsince observations available at the receiver consist of an unknownnumber of echoes, some overlapping, some spurious, that exhibit unknownamplitude, unknown time of arrival and unknown shape. Thus, theconventional TDR technique can demand some modifications of themeasurements setup and more complicated pre-processing as can be seen inK. J. Kerpez, S. Galli, “Single-Ended Loop Make-up Identification—PartI: Improved Algorithms and Performance Results,” IEEE Transactions onInstrumentation and Measurement, vol. 55, no. 2, April 2006.

Another type of single-ended technique for loop-qualification isproposed in T. Bostoen, P. Boets, M. Zekri, L. Van Biesen, T. Pollen andD. Rabijas, “Estimation of the Transfer Function of a Subscriber Loop bymeans of a One-Port Scattering Parameter Measurements at the CentralOffice.” IEEE J. Select. Areas Commun., pp. 936-948, Vol. 20, N^(o) 5,June 2002. According to this reference, it is proposed the use of theone-port scattering parameter S₁₁ to achieve channel transfer functionestimation when a priori information of the loop topology is available.Although this allows good results on short/medium length loops, theassumption that some or all the loop topology is known prior to testingmay limit the practical applicability of this technique. In addition,the technique may present no feasible results, i.e., achievenon-physically loops.

From the G.992.3 and G.992.5 standards for ADSL 2 and ADSL 2+, the loopdiagnostic functionality for modems was standardized, enabling doubleended measurements (DELT). Thus with DELT, the direct loop transferfunction estimation, i.e. the estimation of the attenuation per tone,can be measured without the need of auxiliary techniques. Suchfunctionality is still under test and only a few papers are focused ontransfer function measurements applied on loop makeup identification. InJ. L. Fang, C. Zeng and J. Cioffi, “Bridged Tap Location Estimation,”Electrical Engineering Department, Stanford University, 2003, it isproposed a Bridged-tap location approach from transfer functionmeasurements. But, this method addresses just simple loops with a singlebridge-tap.

As described above, it is desired for telecommunication operators toidentify the complete loop makeup e.g. in order to predict possible bitrates and other performance parameters in the network. However, the SELTand the DELT methods referenced above fail to accurately identify thecomplete loop make up.

SUMMARY

Thus, the object of the present invention is to provide methods andarrangements for identifying the loop makeup.

The object is according to a first aspect achieved by a methodcomprising the steps of receiving a measurement of a SELT parametermeasured at one end of said two ends, receiving a measurement of a DELTloop transfer function measured at said two loop ends; generating amodel for the SELT parameter based on the loop parameters represented bythe vector θ, generating a model for the DELT loop transfer functionbased on the loop parameters represented by the vector θ and

determining the loop parameters represented by the vector θ byminimizing the difference between the model and the measurement of theSELT parameter and by minimizing the difference between the model andthe measurement of the DELT loop transfer function, whereby thedetermined loop parameters are represented by the vector θ that providessaid minimizations.

According to a second aspect of the present invention a loopqualification unit for determining loop parameters describing a topologyof a twisted pair loop, having two ends, for a digital subscriber linesystem, wherein the loop parameters being represented by a vector θcomprising a receiving member for receiving a measurement of a SELTparameter measured at one end of said two ends and for receiving ameasurement of a DELT loop transfer function measured at said two loopends is provided. The loop qualification unit comprises a modelgenerator for generating a model for the SELT parameter based on theloop parameters represented by the vector θ; a model generator forgenerating a model for the DELT loop transfer function based on the loopparameters represented by the vector θ; and a processor for determiningthe loop parameters represented by the vector θ by minimizing thedifference between the model and the measurement of the SELT parameterand by minimizing the difference between the model and the measurementof the DELT loop transfer function, whereby the determined loopparameters are represented by the vector θ that provides saidminimizations.

According to an embodiment the SELT parameter is a one port scatteringparameter S₁₁.

According to a further embodiment the SELT parameter is an inputimpedance Z_(in).

According to a further embodiment, an optimization method of geneticalgorithm is applied for searching for a parameter configuration ofvector θ that minimizes said differences.

According to a further embodiment, said receiving member, the modelgenerators, and the processor are adapted to be operated at least asecond time whereby said measurements are performed in a differentfrequency range.

According to an embodiment the loop parameters comprises at least one ofgauge, length, and type.

Moreover, the unit may is preferably located in a Central Office modeme.g. on a centralized LQ management system. The unit may also be locatedin a customer premise modem, e.g. on a centralized LQ management system.

An advantage with the present invention is that it uses the newestmeasurement setup based on ITU-T G.992.3 and G.992.5 standards, whichprovides fast and accurate DELT measurements. The multi-dimensionalobjective functions, i.e. equations (1) and (2), can be solved usingNSGA-II, which provides an optimization technique that is easy toimplement. NSGA-II is further described in K. Deb, A. Pratap, S. Agarwaland T. Meyarivan, “A fast and elitist multi-objective genetic algorithm:NSGA-II,” Evolutionary Computation, IEEE Transaction on Volume 6, Issue2, April 2002 Page(s): 182-197. Basically, the proposed methodology doesnot need additional pre-processing of the data as techniques based onfor instance TDR.

A further advantage is that the techniques used in the present inventioncan be easily extended and modified. Moreover, improvements can quicklybe integrated. The Genetic Algorithm (GA) concept gives the methodologya high level of flexibility.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates examples of loop parameters to be determined by thepresent invention.

FIG. 2 shows an example of a comparison of the measured and model curvesof the scattering parameters (FIG. 2 a) and the transfer function (FIG.2 b), respectively.

FIG. 3 shows the decoding and calculation procedures of the costfunction using S₁₁ and H(f).

FIG. 4 shows the decoding process.

FIG. 5 shows a GA individual for application in loop makeup in ADSLsystems.

FIG. 6 shows a decoding process of the gauges and length.

FIG. 7 shows a further decoding method for gauge.

FIG. 8 shows the decoding of the number of sections.

FIG. 9 shows the bridge-tap decoding process.

FIG. 10 illustrates a DSL system wherein the present invention may beimplemented and the loop qualification unit according to the presentinvention.

FIG. 11 shows a comparison of the magnitude of the scattering parameterfor ETSI #7 and estimated loop with smallest value of the cost functionof H_(f).

FIG. 12 illustrates a comparison of the magnitude of transfer functionfor ETSI #7 and estimated loop with smallest value of the cost functionof H_(f).

FIG. 13 illustrates a comparison of the magnitude of the scatteringparameter for ETSI #7 and estimated loop with smallest value of the costfunction of S₁₁

FIG. 14 illustrates a comparison of the magnitude of transfer functionfor ETSI #7 and estimated loop with smallest value of the cost functionof S₁₁.

FIG. 15 is a flowchart of the method according to the present invention.

FIG. 16 is a flowchart of one embodiment where GA is used to determinethe loop parameters.

DETAILED DESCRIPTION

In the following description, for purposes of explanation and notlimitation, specific details are set forth, such as particular sequencesof steps, signalling protocols and device configurations in order toprovide a thorough understanding of the present invention. It will beapparent to one skilled in the art that the present invention may bepractised in other embodiments that depart from these specific details.

Moreover, those skilled in the art will appreciate that the functionsexplained herein below may be implemented using software functioning inconjunction with a programmed microprocessor or general purposecomputer, and/or using an application specific integrated circuit(ASIC). It will also be appreciated that while the current invention isprimarily described in the form of methods and devices, the inventionmay also be embodied in a computer program product as well as a systemcomprising a computer processor and a memory coupled to the processor,wherein the memory is encoded with one or more programs that may performthe functions disclosed herein.

As stated above, it is not possible to detect all reflections or echoesby using SELT measurements which results in that the loop makeup cannotbe accurately identified. Further, only DELT measurements do neitherprovide enough information for determining the loop makeup (i.e. theloop parameters). The basic idea with the present invention is tocombine SELT measurements, e.g. measurements of the scatteringparameter, S11 or the input impedance Zin, with the above describedtransfer function obtained by DELT measurements. Hence, according to thepresent invention DELT measurements, i.e. measurements of the transferfunction (H), in combination with SELT measurements of S11 or Zin areused to estimate loop parameters such as type, length, gauge shown inFIG. 1, wherein the loop parameters represent the loop makeup.

Hence, the method and loop qualification unit according to the presentinvention makes it possible to estimate the loop parameters length, looptype and gauge (wire diameter) for each section of the loop, as well asnumber of sections of the loop. The type can be serial or bridged-tap.The length and gauge are given in unit meters or e.g. feet. The loopparameters are represented by a vector θ, e.g. θ=[type, length, gauge]for each section of the loop. According to the present invention, it isrequired to receive both single and double ended measurements of theloop under test e.g. the scattering parameter (S11) obtained by SELT andthe transfer function measurements (H) obtained by DELT.

Thus the loop parameters are determined by receiving a measurement of aSELT parameter and the DELT loop transfer function, generating a modelfor the SELT parameter based on the loop parameters represented by thevector θ, generating a model for the DELT loop transfer function basedon the loop parameters represented by the vector θ and

determining the loop parameters represented by the vector θ byminimizing the difference between the model and the measurement of theSELT parameter and by minimizing the difference between the model andthe measurement of the DELT loop transfer function, whereby thedetermined loop parameters are represented by the vector θ that providessaid minimizations.

It should be noted that the steps above may be performed repeatedlywhereby the measurements are performed in different frequency ranges inorder to achieve even better accuracy.

Therefore, if the used SELT parameter is the scattering parameter S11 inaccordance with one embodiment, the loop parameters are determined byfinding a vector θ that results in an S₁₁ ^(mod el) and an H^(mod el)that generates a minimum for the Mean Squared Error (MSE) defined byfunction V₁(θ) and also for the MSE defined by V₂(θ), wherein S₁₁^(measurement), and H^(measurement) are the measurements of thescattering parameter and the transfer function, respectively.

$\begin{matrix}{{V_{1}(\theta)} = {\sum\limits_{k = 1}^{N}\frac{{{{S_{11}^{model}\left( {f_{k},\theta} \right)} - S_{11}^{measurement}}}^{2}}{\sigma_{S_{11},k}^{2}}}} & {{Equation}\mspace{14mu} (1)} \\{{V_{2}(\theta)} = {\sum\limits_{k = 1}^{N}\frac{{{{H^{model}\left( {f_{k},\theta} \right)} - H^{measurement}}}^{2}}{\sigma_{H,k}^{2}}}} & {{Equation}\mspace{14mu} (2)}\end{matrix}$

As stated above, θ is a parameter array with estimated loop parameterssuch as gauge, length and type as shown in FIG. 1. f_(k) is the k_(th)used frequency tone and N is the number of used tones. For ADSL 2+, themaximum value of N is equal to 512 tones. σ_(S) ₁₁ _(,k) is estimate ofthe variance of the expected value of the kth frequency sample of themeasured scattering parameter and σ_(11,k) is estimate of the varianceof the expected value of the kth frequency sample of the measuredtransfer function. Note that f_(k), used in equations (1) and (2), doesnot necessary correspond to the ADSL 2 or 2+ tone numbers, as FIG. 2also illustrates. The equations (1) and (2) are defined as objectivefunction or cost function related to the optimization process.Essentially, such function quantifies the difference between themeasured quantity and the model of that one. This is done by thesummation of such difference at each tone of the frequency range used.Such sum is called residual of the cost-function. I.e., for every datapoint, the distance vertically from the point to the corresponding pointon the curve fit (the error) is taken, and this value is squared. Thenall those squared values for all data points are added, and divided bythe number of points. The squaring is done to avoid that negative valuesfrom cancelling positive values. The smaller the Mean Squared Error(MSE), the closer the fit is to the data.

If Zin is used instead of S11, S11 is replaced by Zin in the equations(1) and (2).

Thus, the present invention is a method of identification of loop makeupby the optimization of the physical parameters that will provide theminimal residual between the measured quantities and the found loopmodelw2q. I.e. the aim is to find loop parameters for the model curvesuch that the model curves approach the measurement curves. In FIGS. 2 aand 2 b, it is shown an example of a comparison of the measured andmodel curves of the scattering parameters (FIG. 2 a) and the transferfunction (FIG. 2 b), respectively.

The equations (1) and (2) are solved by a unit that may be located atthe CO side, either in the CO modem or, preferably, on a centralized“Loop Qualification (LQ) management system”. Further, the unit may alsobe located at the CPE-side, e.g. on a centralized “Loop Qualification(LQ) management system”.

FIG. 10 illustrates a DSL system wherein the present invention may beimplemented. On the CPE side, is a user modem 1001 connected to theuser's computer (not shown) to the telephone line (twisted pair). themodem 1001 is also connected as illustrated in FIG. 10 to the IP DSLAM(Internet Protocol Digital Subscriber Line Access Multiplexer) 1003which is the equipment that provides xDSL service. The measured data1002 obtained by modem are sent by the twisted-pair to the IP DSLAM1003. Management information 1008 is exchanged between the Ip DSLAM andthe broadband network 1007 by using SNMP (Simple Network ManagementProtocol).

The Line Testing 1004 is sent for PEM (Public Ethernet Manager)Maintenance Office via broadband network. PEM Maintenance Office is thecontrol center that hosts the Loop Topology Identification. LoopTopology Identification 1005 is applied to determine the loop topology(twisted pair) from Line Testing data.

FIG. 10 also shows a modem 1009 in the hybrid circuit, the modem 1009 isconnected 1010 to the hybrid circuit 1011 to the twisted pair loop. Thehybrid circuit comprises a receiving part of the transceiver circuit1013 and a transmitting part 1012 of the transceiver circuit.

Accordingly, the unit 1100 is illustrated in FIG. 10 when implemented inthe CO 1003. The unit comprises comprises a receiving member 1014 forreceiving a measurement of a SELT parameter measured at one end of saidtwo ends, e.g. at the CO and for receiving a measurement of a DELT looptransfer function measured at said two loop ends, e.g. at the CO and atthe CPE. Further, the unit according to the present invention comprisesa model generator 1015 for generating a model for the SELT parameterbased on the loop parameters represented by the vector θ and forgenerating a model for the DELT loop transfer function based on the loopparameters represented by the vector θ. The unit further comprises aprocessor 1016 adapted to determine the loop parameters represented bythe vector θ by minimizing the difference between the model and themeasurement of the SELT parameter and by minimizing the differencebetween the model and the measurement of the DELT loop transferfunction, whereby the determined loop parameters are represented by thevector θ that provides said minimizations.

Since the two objective functions contain multiple unknown parameterse.g. length, gauge, type, number of sections, number of bridge-taps, amulti-dimensional optimization has to be solved. Several optimizationroutines may be applied to this problem. However, according to apreferred embodiment Genetic Algorithms (GA) are used, since it has beenfound by simulation of several test cases that the GA technique is wellsuitable for this application.

A genetic algorithm is a search technique used in computing to find trueor approximate solutions to optimization and search problems. Geneticalgorithms are implemented as a computer simulation in which apopulation of abstract representations called chromosomes or thegenotype of candidate solutions called individuals creatures, orphenotypes to an optimization problem evolves toward better solutions.Traditionally, solutions are represented in binary as strings of 0s and1s, but other encodings are also possible. The evolution usually startsfrom a population of randomly generated individuals belonging to ageneration. In each generation, the fitness of every individual in thepopulation is evaluated, multiple individuals are stochasticallyselected from the current population based on their fitness, andmodified mutated or recombined to form a new population. The newpopulation is then used in the next iteration of the algorithm.

A typical genetic algorithm requires two things to be defined:

-   -   1. a genetic representation of the solution domain,    -   2. a fitness function to evaluate the solution domain.

The representation of the solution domain is in the present inventionthe vector array of loop parameters, i.e. θ.

A typical genetic algorithm requires two things to be defined:

-   -   1. a genetic representation of the solution domain,    -   2. a fitness function to evaluate the solution domain.

The representation of the solution domain is in the present inventionthe vector array of loop parameters, i.e. θ.

The fitness function is defined over the genetic representation andmeasures the quality of the represented solution. The fitness functionis always problem dependent. In the present invention, two fitnessfunctions are used. One fitness function is associated with thescattering parameter, e.g. V₁ and another fitness function is associatedwith the transfer function, e.g. V₂. These fitness functions calculatethe deviation between calculated curves obtained of GA solutions andmeasurement data of those parameters (target curves, i.e. themeasurement). The lower this deviation is, the larger, is the fitness ofthe GA chromosome or GA solution.

Once a genetic representation and a fitness function is defined, GAproceeds to initialize a population of solutions randomly, and thenimproves it through repetitive application of mutation, crossover, andselection operators.

A primary exploration operator used in many genetic algorithms iscrossover. Crossover proceeds in three steps: (1) two individuals arechosen from the population by using the selection operator, and thesetwo structures are considered to be mated; (2) a cross site along thestring length is chosen uniformly at random; and (3) position values areexchanged between the two strings following the cross site. But twoindividuals mate just if a random value associated of these individualsis lower than crossover probability. This random value is obtained ateach generation for each individual's pair.

Mutation is the occasional (low probability, i.e. the mutationprobability has a low value) alteration of a gene that composes the GAindividual. When used together with selection and crossover, mutationacts both as an insurance policy against losing needed diversity. Duringapplication of this operator, random value is obtained for each gene andcompared with mutation probability, if this random value is lower thanmutation probability, the gene value is modified. Otherwise, that geneis not modified.

As stated above, initially many individual solutions are randomlygenerated to form an initial population. The population size depends onthe nature of the problem, but typically contains several hundreds orthousands of possible solutions. Traditionally, the population isgenerated randomly, covering the entire range of possible solutions (thesearch space). Occasionally, the solutions may be “seeded” in areaswhere optimal solutions are likely to be found.

During each successive epoch a proportion of the existing population isselected to breed a new generation. Individual solutions are selectedthrough a fitness-based process, where fitter solutions (as measured bya fitness function) are typically more likely to be selected. Certainselection methods rate the fitness of each solution and preferentiallyselect the best solutions. Other methods rate only a random sample ofthe population, as this process may be very time-consuming.

Most functions are stochastic and designed so that a small proportion ofless fit solutions are selected. This helps keep the diversity of thepopulation large, preventing premature convergence on poor solutions.Popular and well-studied selection methods include roulette wheelselection and tournament selection.

The next step is to generate a second generation population of solutionsfrom those selected through genetic operators: crossover (also calledrecombination), and/or mutation.

For each new solution to be produced, a pair of “parent” solutions isselected for breeding from the pool selected previously. By producing a“child” solution using the above methods of crossover and mutation, anew solution is created which typically shares many of thecharacteristics of its “parents”. New parents are selected for eachchild, and the process continues until a new population of solutions ofappropriate size is generated.

These processes ultimately result in the next, generation population ofchromosomes that is different from the initial generation. Generally theaverage fitness will have increased by this procedure for thepopulation, since only the best organisms from the first generation areselected for breeding, along with a small proportion of less fit forreasons already mentioned above.

This generational process is repeated until a termination condition hasbeen reached. In the present invention the, process is terminated when asolution is found that satisfies minimum criteria.

The control of the GA parameters in relation to the present inventionwill now be described.

Mutation process consists of the generation of a random value for eachgene and the comparison of them with mutation probability. if the randomvalue is lower than mutation probability, the gene value is modified.Otherwise, the gene is kept untouched.

The main GA operands that lead the optimization process are mutationprobability and crossover probability. It is important to find optimumvalues for these operands in order to obtain reasonable results afterthe optimization process. However, basically this will depend on theproblem under study. An attempt to become GA self-adaptive regarding tothis issue is developed for this invention. It consists in discoveringan optimal configuration for such operands at each iteration of theoptimization process. To accomplish it, a sweeping on the valuesassociated to GA operands is done, starting from high values (around90-85%). This sweeping is conditioned to the evolution of theindividuals' fitness: values that induce successive improvements on thefitness of the population are maintained until they do not provideimprovement anymore. (In the GA terminology, the inverse of the residualassociated to a certain cost-function is called fitness.) At this time,the current values of GA operands are decreased. Additionally, wheneversome evolution on the elite individual's fitness occurs, the totalnumber of generations is increased, providing more time to the algorithmto find the global minimum. Since the alteration of the GA operands'values is related to the evolution of the individuals' fitness, thefinal values for these operands can vary from one simulation to another.This process is improving the GA convergence, reducing the probabilityof getting stuck in a local minima. Basically, this process is used tocontrol the GA parameters, i.e. the mutation and crossoverprobabilities. The process is shown in more details below. probcross andprobmut are the current values of crossover and mutation probabilities,fitness is the vector with the fitness values of the population (calledpop in this case) and calcfitness function is used to calculate thisvector. countgen variable counts the number of generations with noevolution of the best individual's fitness. If fitelit (the fitness ofbest individual) is lesser than maximum fitness of population in ageneration, the countgen variable is incremented by 1. Otherwise, i.e.if the best individual has evolved, the total number of generation to becarried out (ngeneration) is increased by gener (varying from 20 to 25),the countgen is reset with 0 and fitelit is updated with the maximumfitness in the current generation. But, if there is not evolution of GApopulation during a certain number of generations, given by theexpression perc*(ngeneration−i) (where perc is a perceptual valuevarying from 5% to 10%), the probcross and probmut have their valuesdecreased by the expression:

prob=prob−prob×δ

where prob is the current value of mutation or crossover probabilitiesand δ is the perceptual value used to decrease such probabilities. Thelatter parameter is configured in the beginning of the optimizationprocess. After that, it is applied the GA operators mutation andcrossover, using this new values of those probabilities.

In FIG. 3, it is shown a diagram with decoding and evaluating process ofthe GA individuals. Each individual from GA represents a complete loopwith information about number of sections, gauge and length of eachsection, number of bridge taps and its position. Therefore, anindividual is a representation in the GA scope of the makeup structure(or θ). The popsize (size of GA population) number of individuals aredecoded into their corresponding loop makeup and they are evaluated forthe objective functions. The value of each cost function is useddirectly to qualify the individuals generated within Genetic Algorithms(GA). Therefore, to each individual is associated a vectorV(θ)=[V₁(θ)V₂(θ)] containing the error mean square for each targetcurve. In the embodiment of the present invention, two target curves areused: the measured scattering and transfer function parameters. For eachGA solution, these parameters can be calculated using a cable model, asfor instance the VUB0 and BT. In principle, both line models can beapplied to the GA concept. GA uses these objective function values toapply their operators in order to generate offspring of larger fitness,i.e., smaller values of the error mean square. For the application ofthe embodiment of the invention the NSGA-II for multi-objectiveoptimization is used.

The important process in GA optimization is the decoding process.Basically, decoding process connects the GA process to the physicalproblem under optimization. The decoding process convert GA chromosometo the physical parameter. In case of the loop makeup application, thechromosome is converted to the makeup structure shown in FIG. 1. In FIG.4, it is shown the decoding process, where each real number array isconverted to loops, accordingly to their data, as shown on left side ofFIG. 4. The coding/decoding strategies applied to GA are describedbelow.

The chromosome is composed as shown in FIG. 5. For each section,normalized value of the length and gauge are within the sub-arrayssection₁-section_(maxnsecs). The value N determines the number ofsections varying from its minimum up to its maximum value (which can beconfigured by the user). M is the number of bridge-taps, which dependson the number of sections, and BT₂ up to BT_(nsecs) indicates theposition of these M bridge-taps.

The decoding process transforms the GA data into parameters of thephysical problem under optimization, as explained previously. In FIG. 6,it is shown the decoding of the length and gauge—which is a simpleprocess. Thus, for instance, the length values can assume any real valuebetween length_(min) and length_(max). These values can be defined byuser. This assures that the values are within a feasible range. However,another restrictive method can be used for decoding of the gaugesbecause the gauges values can not vary continuously and can assumestandard values. FIG. 7 describes this method. Basically, the intervalbetween zero and one is divided into ngt sub-intervals (called ingeneric way of N), where ngt is the number of gauge values that will beused during the optimization process. Thus, the coding value of thegauge of each section is directly compared with each sub-interval. Thisprocedure is repeated until the sub-interval that represents the gaugeis found, as shown in FIG. 7, and thus, its gauge value is determined.Both decoding methods for gauge can be used.

A possible method for the decoding process of the number of sections isshown in FIG. 8. The codified value of the number of sections could be,for instance, in the penultimate position of the individual vector—orpenultimate gene of the chromosome in GA terminology (FIG. A4). Thevalues 0.5 and 1.0, shown in FIG. 8, are related to all values ofsection from minsecs and maxnsecs having the same probability to occur.The values are integer values; they are rounded by Matlab™ via its roundfunction. In this way, nsecs sections are considered to compose makeupstructure. For instance, in FIG. 8, nsecs is equal to 3 and thus, allothers sections starting from the fourth section are not taken intoaccount in the calculation of the S₁₁ and of the H(f). Basically, thedecoding method of the number of bridge-tap is also in the same way. InFIG. 9, nsecbt is the number of sections that can be a bridge-tap; asjust the first section can not be bridged-tap, this parameter is equalto nsecs−1. Such bridge-taps should be alternate, thus the maximumnumber of bridge-taps (maxnbt) should be half of nsecbt.

Information about the number of bridge-taps is codified in the last gene( N) and to decode it the same equation for decoding the number ofsections (nsecs) is used. The position of the first bridge-tap(posfirstbt) is the maximum value of the genes BT₂ to BT_(nsecs), andthe position of the other bridge-taps are determined as a function ofthe position of the first bridge-tap, of course, considering thealternation between bridge-taps.

Other method for gauge decoding is the gaugesort. In previous paragraph,in decoding process of the gauges, the values of gauges could occur inany order and with repetition of its values. For some cases, especiallyfor large number of sections, it appeared to be difficult theconvergence of the optimization algorithm. In the majority of cases (atleast), the gauge values found in the recommendation increase from thecentral office to the costumer premises and they do not repeat. In thatway, a modification was implemented on the method described in FIG. 7.In this method, the number of sub-intervals for each section isdependent on an integer value set by user that determines the number ofavailable gauges, the number of sections, the position in the gaugevector and the number of previously decoding sections. The code for thistechnique is shown below:

for i=1:nsecs     N = ( ngst − jlast ) − nsecs + i;     delta = 1 / N;    for j=1:N       if( ind(2*i) <= j*delta )         makeup(i).gauge =stgauge(j + jlast);         jlast = jlast + j;         break;      end    end endThe parameter j_(last) determines the last position of the gauge vector(stgauge) used. Basically, the number of sub-interval of each section(that it will define the number of gauges) is equal to the number ofvalues of available gauge less the number of section that still are tobe decoded, and not considering the values of gauges already used. Inthat way, the application of this technique avoids the repetition ofgauges and assures that they are always growing. However, for thismethod to work, it is necessary that the number of gauges is at leastequal to the number of sections. This method can also be disabled andenabled by user. This method accelerates the GA convergence for ETSIscenarios.

Below is shown the result obtained by the GA methodology according tothe embodiment of the invention when S₁₁ is used as the SELTmeasurement.

ETSI test loops are defined in ITU-T G.996.1 “Test procedures fordigital subscriber line (DSL) transceivers”. From the values of thetransfer function and scattering parameter of the ETSI #7 scenario, asimulation was carried out to obtain its parameters. Table 1 shows thetrue values of the parameters for scenario ETSI loop #7.

TABLE 1 Scenario ETSI #7 ETSI #7 (I_loss = 36) Type Serial Serial SerialLength (km) 0.20 0.60 4.00 Gauge (mm) 0.32 0.40 0.90

The GA results are shown in Table 2. Table 3 contains estimations of thesmallest value of the scattering parameter and estimative of thesmallest value of the transfer function, respectively. The error of theestimative remains below 2e-5 dB for S₁₁ and 3.15e-5 dB for H(f) as canbe seen in FIGS. 11 to 14. FIG. 11 shows a comparison of the magnitudeof the scattering parameter for ETSI #7 and estimated loop with smallestvalue of the cost function of H_(f..) FIG. 12 illustrates a comparisonof the magnitude of transfer function for ETSI #7 and estimated loopwith smallest value of the cost function of H_(f). FIG. 13 illustrates acomparison of the magnitude of the scattering parameter for ETSI #7 andestimated loop with smallest value of the cost function of S₁₁. FIG. 14illustrates a comparison of the magnitude of transfer function for ETSI#7 and estimated loop with smallest value of the cost function of S₁₁

TABLE 2 Values of the estimated parameters ETSI #7 (I_loss = 36) TypeSerial Serial Serial Length (km) 0.19997 0.60012 3.99991 Gauge (mm) 0.320.40 0.90

TABLE 3 Values of the estimated parameters ETSI #7 (I_loss = 36) TypeSerial Serial Serial Length (km) 0.19997 0.60012 4.00012 Gauge (mm) 0.320.40 0.90

Thus, the present invention relates to a method illustrated by theflowchart of FIG. 15. The method comprises the steps of:

1501. Generate a model for the SELT parameter based on the loopparameters represented by the vector θ.

1502. Generate a model for the DELT loop transfer function based on theloop parameters represented by the vector θ.

1503. Determine the loop parameters represented by the vector θ byminimizing the difference between the model and the measurement of theSELT parameter and by minimizing the difference between the model andthe measurement of the DELT loop transfer function, whereby thedetermined loop parameters are represented by the vector θ that providessaid minimizations.

According to an embodiment, GA may be used for determining the loopparameters. The flowchart of FIG. 16 illustrates that embodiment.

1601. Initialize a population.

1602. Calculate cost functions, e.g. V₁ and V₂.

1603. The cost functions are solved by using NSGA-II.

1604. GA process stop criterion

1605. Select individuals from GA population to compose a new population

1606. Apply the crossover operator to the selected individuals

1607. Apply the mutation operator to the selected individuals

While the present invention has been described with respect toparticular embodiments (including certain device arrangements andcertain orders of steps within various methods), those skilled in theart will recognize that the present invention is not limited to thespecific embodiments described and illustrated herein. Therefore, it isto be understood that this disclosure is only illustrative. Accordingly,it is intended that the invention be limited only by the scope of theclaims appended hereto.

1-20. (canceled)
 21. A method for determining loop parameters describinga topology of a twisted pair loop, having two ends, for a digitalsubscriber line system, wherein the loop parameters being represented bya vector θ comprising the steps of: receiving a measurement of a SingleEnded Line Test (SELT) parameter measured at one end of the two ends;receiving a measurement of a Double Ended Line Test (DELT) loop transferfunction measured at the two loop ends; generating a first model for theSELT parameter based on the loop parameters represented by the vector θ;generating a second model for the DELT loop transfer function based onthe loop parameters represented by the vector θ; and determining theloop parameters represented by the vector θ by minimizing the differencebetween the first model and the measurement of the SELT parameter and byminimizing the difference between the second model and the measurementof the DELT loop transfer function, wherein the determined loopparameters are represented by the vector θ that provides theminimizations.
 22. The method according to claim 21, wherein the SELTparameter is a one port scattering parameter S₁₁.
 23. The methodaccording to claim 21, wherein the SELT parameter is an input impedanceZ_(in).
 24. The method according to claim 21, wherein the determiningstep further comprises the step of: applying an optimization method ofgenetic algorithm searching for a parameter configuration of vector θthat minimizes the differences.
 25. The method according to claim 21,wherein the steps are performed at least a second time in which themeasurements are performed in a different frequency range.
 26. Themethod according to claim 21, wherein the loop parameters comprises atleast one of gauge, length, and type.
 27. The method according to claim21, wherein the steps are performed in a Central Office modem.
 28. Themethod according to claim 27, wherein the steps are performed in aCentral Office modem on a centralized Loop Qualification (LQ) managementsystem.
 29. The method according to claim 21, wherein the steps areperformed in a customer premise modem.
 30. The method according to claim29, wherein the steps are performed in a Central premise modem on acentralized Loop Qualification (LQ) management system.
 31. A loopqualification unit for determining loop parameters describing a topologyof a twisted pair loop, having two ends, for a digital subscriber linesystem, wherein the loop parameters are represented by a vector θcomprising a receiving member for receiving a measurement of a SingleEnded Line Test (SELT) parameter measured at one end of the two ends andfor receiving a measurement of a Double Ended Line Test (DELT) looptransfer function measured at the two loop ends, said loop qualificationunit comprising: a first model generator for generating a first modelfor the SELT parameter based on the loop parameters represented by thevector θ; a second model generator for generating a second model for theDELT loop transfer function based on the loop parameters represented bythe vector θ; and a processor for determining the loop parametersrepresented by the vector θ by minimizing the difference between thefirst model and the measurement of the SELT parameter and by minimizingthe difference between the second model and the measurement of the DELTloop transfer function, wherein the determined loop parameters arerepresented by the vector θ that provides the minimizations.
 32. Theloop qualification unit according to claim 31, wherein the SELTparameter is a one port scattering parameter S₁₁.
 33. The loopqualification unit according to claim 31, wherein the SELT parameter isan input impedance Z_(in).
 34. The loop qualification unit according toclaim 31, wherein the processor further comprises means for applying anoptimization method of genetic algorithm searching for a parameterconfiguration of vector θ that minimizes the differences.
 35. The loopqualification unit according to claim 31, wherein the receiving member,the model generators, and the processor are adapted to be operated atleast a second time in which the measurements are performed in adifferent frequency range.
 36. The loop qualification unit according toclaim 31, wherein the loop parameters comprise at least one of gauge,length, and type.
 37. The loop qualification unit according to claim 31,wherein the unit is located in a Central Office modem.
 38. The loopqualification unit according to claim 37, wherein the unit is located ina Central Office modem on a centralized Loop Qualification (LQ)management system.
 39. The loop qualification unit according to claim31, wherein the unit is located in a customer premise modem.
 40. Theloop qualification unit according to claim 39, wherein the unit islocated in a Central premise modem on a centralized Loop Qualification(LQ) management system.